Skip to main content

Coatings & Corrosion Management

The Impact of Corrosion

In 2023, corrosion resulted in over $5 billion in costs for the US Air Force and more than $8.6 billion for the US Navy, excluding expenses related to support equipment and vehicles. Understanding material corrosion is essential for selecting and qualifying materials used in aerospace components, vehicles, oil and gas pipelines, facilities, and other infrastructure. UDRI’s corrosion science and engineering experts collaborate with partners to characterize materials for various applications, providing insights into cost-effective maintenance and support for critical systems operating in the harshest environments.

 

Cost of Corrosion in 2023

$5 Billion

US Air Force

$8.6 Billion

US Navy

Testing & Qualification of Coatings

UDRI expert scientists and engineers provide research, development, testing, evaluation, qualification, integration and sustainment planning for advanced aerospace and industrial materials.

Areas of Expertise

  • Corrosion Science
  • Coatings Testing & Qualification
  • Test Method Development
  • Materials Characterization & Testing
  • Process Evaluation & Improvement
  • Erosion-Resistant Materials
  • Accelerated Environmental Simulation Testing
${ Large military aircraft being washed to prevent corrosion. }

Proven Partnership & Support

UDRI has been supporting US Air Force corrosion, erosion and sustainment initiatives for over 23 years, developing unique experience in operating and testing erosion equipment such as the Hot Erosion Rig (HER) and the Supersonic Rain Erosion Rig (SuRE) at Wright-Patterson Air Force Base.

Accreditation & Experience

UDRI has continually maintained ISO/IEC 17025 accreditation and SAE AS-5505 certification since 2001. These certifications cover all testing required to qualify of Air Force Outer Mold Line (OML) and Inner Mold Line (IML) coatings. Our team of experienced professionals and expert technicians solve M&P, coating, corrosion, specialty material and erosion challenges, help develop and integrate novel, improved and cost-saving materials, methods and equipment, and perform research resulting in significant cost savings and long-lasting impacts.

Emerging Technologies & Innovations

UDRI is a key stakeholder in corrosion modeling efforts taking place across the Department of Defense aimed at developing comprehensive data sets for modeling and simulation for corrosion planning and has previously served as a validation test bed for other modeling efforts.

Machine Learning & Data Analytics

UDRI’s Machine Learning (ML) and Data Analytics teams have worked together to develop guided tools for Air Force customers. These tools consist of interactive dashboards using machine learning and large language models (LLM) leveraging aircraft maintenance data to identify special areas of interest for corrosion stakeholders. Insights from these dashboards can be used by our experts to hone in on issues before they visit facilities so that time can be more valuably spent on site solving corrosion problems instead of identifying them. This allows for targeted efforts to address consistent, costly corrosion problems across multiple airframes that directly impact maintenance manhours and aircraft downtime.

Machine Learning Algorithms

UDRI-developed machine learning algorithms improve user awareness of available data, suggest areas for further investigation and make connections between users and information to ensure analyses have more breadth and depth than a single user working alone.

  • Vision-based systems to verify the line-tool position and orientation
  • Atomization characterization on shadowgraph images for spray applications

  • Data science and machine learning approaches for optimizing flight design parameters
  • Planning and content recommendation systems using combinations of expert systems with ML models

  • Maintenance log text correction, standardization and correlation using an ensemble of probabilistic models, supervised learning and unsupervised learning
  • Video summarization and organization by leveraging computer vision, audio processing and natural language processing technologies

  • Detection and characterization of Coordinate Measuring Machine (CMM) probes
  • Detection of distortions and defects in additive manufacturing processes from in-situ tomography, thermal or electro-optic data

  • Supply chain risk prediction using a suite of ML models
  • Probabilistic learning for the prediction of solar energy production

${ ACES actuators }

Testing, Characterization & Evaluation

Our expert research and engineering teams conduct comprehensive suites of testing, characterization and inspection scenarios to evaluate the structural integrity and maintenance of materials, components and structures.

The appearance of U.S. Department of Defense (DoD) visual information does not imply or constitute DoD endorsement.